With the ever-increasing rate of medical innovation and therapeutic discovery comes the unprecedented challenge of effectively and accurately analyzing clinical data.1,2 In order to assess the efficacy of potential medical treatments, it is important to use a standardized method of comparing outcomes from an experimental population to outcomes from a control population. Historically, clinical investigators have used the concepts of statistical significance and numerical thresholds (P-values) to compare 2 populations, allowing clinicians to determine whether a treatment successfully altered an outcome measurement.3 Although statistical significance and predefined P-values have demonstrated their efficacy in the field of research for nearly 70 yr, these measures are not ideally suited to the clinical setting due to their high propensity of manipulation. Statistical significance can be achieved solely by increasing the population size.4 While a small difference in outcome between 2 large populations can be identified as statistically significant, a comparably large difference in outcome between 2 smaller populations can conversely be interpreted as failing to reach statistical significance.5,6 The ability for factors such as sample size to influence the determination of treatment efficacy emphasizes the critical need for an outcome measure reliant on the patient experience rather than data exclusively.